Appl. Comput. Intell. Soft Comput.最新文献_第9页

Caption Generation Based on Emotions Using CSPDenseNet and BiLSTM with Self-Attention 基于自关注的CSPDenseNet和BiLSTM的情绪标题生成

Appl. Comput. Intell. Soft Comput. Pub Date : 2022-09-17 DOI: 10.1155/2022/2756396

K. S., Pon Karthika K, Jayakumar Kaliappan, S. Selvaraj, Nagalakshmi R, Baye Molla

{"title":"Caption Generation Based on Emotions Using CSPDenseNet and BiLSTM with Self-Attention","authors":"K. S., Pon Karthika K, Jayakumar Kaliappan, S. Selvaraj, Nagalakshmi R, Baye Molla","doi":"10.1155/2022/2756396","DOIUrl":"https://doi.org/10.1155/2022/2756396","url":null,"abstract":"Automatic image caption generation is an intricate task of describing an image in natural language by gaining insights present in an image. Featuring facial expressions in the conventional image captioning system brings out new prospects to generate pertinent descriptions, revealing the emotional aspects of the image. The proposed work encapsulates the facial emotional features to produce more expressive captions similar to human-annotated ones with the help of Cross Stage Partial Dense Network (CSPDenseNet) and Self-attentive Bidirectional Long Short-Term Memory (BiLSTM) network. The encoding unit captures the facial expressions and dense image features using a Facial Expression Recognition (FER) model and CSPDense neural network, respectively. Further, the word embedding vectors of the ground truth image captions are created and learned using the Word2Vec embedding technique. Then, the extracted image feature vectors and word vectors are fused to form an encoding vector representing the rich image content. The decoding unit employs a self-attention mechanism encompassed with BiLSTM to create more descriptive and relevant captions in natural language. The Flickr11k dataset, a subset of the Flickr30k dataset is used to train, test, and evaluate the present model based on five benchmark image captioning metrics. They are BiLingual Evaluation Understudy (BLEU), Metric for Evaluation of Translation with Explicit Ordering (METEOR), Recall-Oriented Understudy for Gisting Evaluation (ROGUE), Consensus-based Image Description Evaluation (CIDEr), and Semantic Propositional Image Caption Evaluation (SPICE). The experimental analysis indicates that the proposed model enhances the quality of captions with 0.6012(BLEU-1), 0.3992(BLEU-2), 0.2703(BLEU-3), 0.1921(BLEU-4), 0.1932(METEOR), 0.2617(CIDEr), 0.4793(ROUGE-L), and 0.1260(SPICE) scores, respectively, using additive emotional characteristics and behavioral components of the objects present in the image.","PeriodicalId":8218,"journal":{"name":"Appl. Comput. Intell. Soft Comput.","volume":"36 1","pages":"2756396:1-2756396:13"},"PeriodicalIF":0.0,"publicationDate":"2022-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82831644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

AI-Enabled Ant-Routing Protocol to Secure Communication in Flying Networks 基于ai的反路由协议在飞行网络中的安全通信

Appl. Comput. Intell. Soft Comput. Pub Date : 2022-09-16 DOI: 10.2139/ssrn.4185477

Sadoon Hussain, Ahmed Sami, Abida Thasin, Redhwan M. A. Saad

引用次数: 0

A Proposed Design Method for Sparse Array Antenna by Using the Spacing Coefficient Algorithm 基于间隔系数算法的稀疏阵列天线设计方法

Appl. Comput. Intell. Soft Comput. Pub Date : 2022-09-14 DOI: 10.1155/2022/4050068

E. Sandi, Aodah Diamah, P. Yuliatmojo, B. Maruddani

引用次数: 0

Energy-Aware JPEG Image Compression: A Multi-Objective Approach 能量感知JPEG图像压缩:一种多目标方法

Appl. Comput. Intell. Soft Comput. Pub Date : 2022-09-09 DOI: 10.48550/arXiv.2209.04374

S. J. Mousavirad, Luís A. Alexandre

{"title":"Energy-Aware JPEG Image Compression: A Multi-Objective Approach","authors":"S. J. Mousavirad, Luís A. Alexandre","doi":"10.48550/arXiv.2209.04374","DOIUrl":"https://doi.org/10.48550/arXiv.2209.04374","url":null,"abstract":"Customer satisfaction is crucially affected by energy consumption in mobile devices. One of the most energy-consuming parts of an application is images. While different images with different quality consume different amounts of energy, there are no straightforward methods to calculate the energy consumption of an operation in a typical image. This paper, first, investigates that there is a correlation between energy consumption and image quality as well as image file size. Therefore, these two can be considered as a proxy for energy consumption. Then, we propose a multi-objective strategy to enhance image quality and reduce image file size based on the quantisation tables in JPEG image compression. To this end, we have used two general multi-objective metaheuristic approaches: scalarisation and Pareto-based. Scalarisation methods find a single optimal solution based on combining different objectives, while Pareto-based techniques aim to achieve a set of solutions. In this paper, we embed our strategy into five scalarisation algorithms, including energy-aware multi-objective genetic algorithm (EnMOGA), energy-aware multi-objective particle swarm optimisation (EnMOPSO), energy-aware multi-objective differential evolution (EnMODE), energy-aware multi-objective evolutionary strategy (EnMOES), and energy-aware multi-objective pattern search (EnMOPS). Also, two Pareto-based methods, including a non-dominated sorting genetic algorithm (NSGA-II) and a reference-point-based NSGA-II (NSGA-III) are used for the embedding scheme, and two Pareto-based algorithms, EnNSGAII and EnNSGAIII, are presented. Experimental studies show that the performance of the baseline algorithm is improved by embedding the proposed strategy into metaheuristic algorithms.","PeriodicalId":8218,"journal":{"name":"Appl. Comput. Intell. Soft Comput.","volume":"69 1","pages":"110278"},"PeriodicalIF":0.0,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75894292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

On the exact l1 penalty function method for convex nonsmooth optimization problems with fuzzy objective function 具有模糊目标函数的凸非光滑优化问题的精确l1罚函数法

Appl. Comput. Intell. Soft Comput. Pub Date : 2022-09-07 DOI: 10.1007/s00500-022-07459-0

T. Antczak

引用次数: 1

Overall Cost Overrun Estimate in Residential Projects: A Hybrid Dynamics Approach 住宅项目总体成本超支估算:一种混合动力学方法

Appl. Comput. Intell. Soft Comput. Pub Date : 2022-09-05 DOI: 10.1155/2022/2285971

Ghada Taha, A. Sherif, Mohamed Badawy

{"title":"Overall Cost Overrun Estimate in Residential Projects: A Hybrid Dynamics Approach","authors":"Ghada Taha, A. Sherif, Mohamed Badawy","doi":"10.1155/2022/2285971","DOIUrl":"https://doi.org/10.1155/2022/2285971","url":null,"abstract":"Residential projects are described as complex, dynamic systems that are subject to uncertainty. Cost performance is a fundamental challenge. As a result, project managers must adequately identify risks that might lead to cost overruns in residential construction projects. Simulation is noticed to be a useful technique for dealing with these complications. Therefore, this study developed a hybrid dynamic approach to study the effect of different risks on the cost performance of construction projects. The proposed approach combines system dynamics (SD) and discrete event simulation (DES), which can take into consideration the dynamics of the project environment, which contains various continuous influencing factors as well as the construction operations. The developed hybrid model is validated through serial model structure tests and model behavior tests, with the aid of data collected from a real construction project used in the simulation process. Based on the simulation results, it is concluded that the proposed hybrid dynamic approach is helpful to enhance the process performance by permitting construction managers to identify possible process improvement areas that traditional methods may miss.","PeriodicalId":8218,"journal":{"name":"Appl. Comput. Intell. Soft Comput.","volume":"18 1","pages":"2285971:1-2285971:17"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73981984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DPb-MOPSO: A Dynamic Pareto bi-level Multi-objective Particle Swarm Optimization Algorithm DPb-MOPSO:一种动态Pareto双级多目标粒子群优化算法

Appl. Comput. Intell. Soft Comput. Pub Date : 2022-09-01 DOI: 10.2139/ssrn.4047845

A. Aboud, N. Rokbani, R. Fdhila, A. Qahtani, Omar Almutiry, Habib Dhahri, Amir Hussain, A. Alimi

引用次数: 8

Improving the Bi-LSTM model with XGBoost and attention mechanism: A combined approach for short-term power load prediction 利用XGBoost和注意机制改进Bi-LSTM模型:一种短期电力负荷预测的组合方法

Appl. Comput. Intell. Soft Comput. Pub Date : 2022-09-01 DOI: 10.2139/ssrn.4117249

Yeming Dai, Qiong Zhou, Mingming Leng, Xinyu Yang, Yanxin Wang

引用次数: 19

Mayfly Taylor Optimisation-Based Scheduling Algorithm with Deep Reinforcement Learning for Dynamic Scheduling in Fog-Cloud Computing 基于Mayfly Taylor优化和深度强化学习的雾云动态调度算法

Appl. Comput. Intell. Soft Comput. Pub Date : 2022-08-28 DOI: 10.1155/2022/2131699

G. Shruthi, Monica R. Mundada, B. J. Sowmya, S. Supreeth

{"title":"Mayfly Taylor Optimisation-Based Scheduling Algorithm with Deep Reinforcement Learning for Dynamic Scheduling in Fog-Cloud Computing","authors":"G. Shruthi, Monica R. Mundada, B. J. Sowmya, S. Supreeth","doi":"10.1155/2022/2131699","DOIUrl":"https://doi.org/10.1155/2022/2131699","url":null,"abstract":"Fog computing domain plays a prominent role in supporting time-delicate applications, which are associated with smart Internet of Things (IoT) services, like smart healthcare and smart city. However, cloud computing is a capable standard for IoT in data processing owing to the high latency restriction of the cloud, and it is incapable of satisfying needs for time-sensitive applications. The resource provisioning and allocation process in fog-cloud structure considers dynamic alternations in user necessities, and also restricted access resources in fog devices are more challenging. The global adoption of IoT-driven applications has led to the rise of fog computing structure, which permits perfect connection for mobile edge and cloud resources. The effectual scheduling of application tasks in fog environments is a challenging task because of resource heterogeneity, stochastic behaviours, network hierarchy, controlled resource abilities, and mobility elements in IoT. The deadline is the most significant challenge in the fog computing structure due to the dynamic variations in user requirement parameters. In this paper, Mayfly Taylor Optimisation Algorithm (MTOA) is developed for dynamic scheduling in the fog-cloud computing model. The developed MTOA-based Deep Q-Network (DQN) showed better performance with energy consumption, service level agreement (SLA), and computation cost of 0.0162, 0.0114, and 0.0855, respectively.","PeriodicalId":8218,"journal":{"name":"Appl. Comput. Intell. Soft Comput.","volume":"74 1","pages":"2131699:1-2131699:17"},"PeriodicalIF":0.0,"publicationDate":"2022-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82240684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

LSTM-Based Neural Network to Recognize Human Activities Using Deep Learning Techniques 基于lstm的神经网络识别人类活动的深度学习技术

Appl. Comput. Intell. Soft Comput. Pub Date : 2022-08-23 DOI: 10.1155/2022/1681096

Sunitha Sabbu, V. Ganesan

{"title":"LSTM-Based Neural Network to Recognize Human Activities Using Deep Learning Techniques","authors":"Sunitha Sabbu, V. Ganesan","doi":"10.1155/2022/1681096","DOIUrl":"https://doi.org/10.1155/2022/1681096","url":null,"abstract":"Deep learning techniques have recently demonstrated their ability to be applied in any field, including image processing, natural language processing, speech recognition, and many other real-world problem-solving applications. Human activity recognition (HAR), on the other hand, has become a popular research topic due to its wide range of applications. The researchers began working on the new ideas by combining the two emerging areas to solve HAR problems using deep learning. Recurrent neural networks (RNNs) in deep learning (DL) provide higher opportunity in recognizing the abnormal behavior of humans to avoid any kind of security issues. The present study proposed a deep network architecture based on one of the techniques of deep learning named as residual bidirectional long-term memory (LSTM). The new network is capable of avoiding gradient vanishing in both temporal and spatial dimensions with a view to increase the rate of recognition. To understand the complexity of activities recognition and classification, two LSTM models, basic model and the proposed model, were used. Later, a comparative analysis is performed to understand the efficiencies of the models during the classification of five human activities like abuse, arrest, arson, assault, and fighting images classification. The basic LSTM model has achieved a training accuracy of just 18% and testing accuracy of 21% with higher training and classification loss values. But the proposed LSTM model has outperformed the basic model while achieving 100% classification accuracy. Finally, the observations have proved that the proposed LSTM model is best suitable in recognizing and classifying the human activities well even for real-time videos.","PeriodicalId":8218,"journal":{"name":"Appl. Comput. Intell. Soft Comput.","volume":"2017 1","pages":"1681096:1-1681096:8"},"PeriodicalIF":0.0,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86195528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1